Multiple Instance Learning (MIL) [1]}, [2]}, [3]} generalizes the supervised learning framework to bags of inputs. Formally, recall that \({\mathcal {X}}\)  and \({\mathcal {Y}}\)  are input and output domains, and let \(X = (x^{(1)}, x^{(2)}, \dots , x^{(r)}) \in {\mathcal {X}}^r\) , \(r \in {\mathbb {N}}\)  indicate a bag with \(r\)  instances. Furthermore, the MIL paradigm assumes that the bag-level response \(Y \in {\mathcal {Y}}\)  is a known function of the instance-level responses \(y^{(1)}, y^{(2)}, \dots , y^{(r)}\) , which can encompass a wide variety of choices [4]}, [5]}, [6]}, [7]}, [8]}, [9]}.
